Following the last post, which focused on an assertion about crowdsourced geographic information and citizen science I continue with another observation. As was noted in the previous post, these can be treated as ‘laws’ as they seem to emerge as common patterns from multiple projects in different areas of activity – from citizen science to crowdsourced geographic information. The first assertion was about the relationship between the number of volunteers who can participate in an activity and the amount of time and effort that they are expect to contribute.

This time, I look at one aspect of data quality, which is about consistency and coverage. Here the following assertion applies:

‘All information sources are heterogeneous, but some are more honest about it than others’

There are many aspects for these concerns, so the assertion deals with the aspects of comprehensiveness and consistency which are used as a reason to dismiss crowdsourced information when comparing them to authoritative data. However, at a closer look we can see that all these information sources are fundamentally heterogeneous. Despite of all the effort to define precisely standards for data collection in authoritative data, heterogeneity creeps in because of budget and time limitations, decisions about what is worthy to collect and how, and the clash between reality and the specifications. Here are two examples:

Take one of the Ordnance Survey Open Data sources – the map present themselves as consistent and covering the whole country in an orderly way. However, dig in to the details for the mapping, and you discover that the Ordnance Survey uses different standards for mapping urban, rural and remote areas. Yet, the derived products that are generalised and manipulated in various ways, such as Meridian or Vector Map District, do not provide a clear indication which parts originated from which scale – so the heterogeneity of the source disappeared in the final product.

The census is also heterogeneous, and it is a good case of specifications vs. reality. Not everyone fill in the forms and even with the best effort of enumerators it is impossible to collect all the data, and therefore statistical analysis and manipulation of the results are required to produce a well reasoned assessment of the population. This is expected, even though it is not always understood.

Therefore, even the best information sources that we accept as authoritative are heterogeneous, but as I’ve stated, they just not completely honest about it. The ONS doesn’t release the full original set of data before all the manipulations, nor completely disclose all the assumptions that went into reaching the final value. The Ordnance Survey doesn’t tag every line with metadata about the date of collection and scale.

Somewhat counter-intuitively, exactly because crowdsourced information is expected to be inconsistent, we approach it as such and ask questions about its fitness for use. So in that way it is more honest about the inherent heterogeneity.

Importantly, the assertion should not be taken to be dismissive of authoritative sources, or ignoring that the heterogeneity within crowdsources information sources is likely to be much higher than in authoritative ones. Of course all the investment in making things consistent and the effort to get universal coverage is indeed worth it, and it will be foolish and counterproductive to consider that such sources of information can be replaced as is suggest for the census or that it’s not worth investing in the Ordnance Survey to update the authoritative data sets.

Moreover, when commercial interests meet crowdsourced geographic information or citizen science, the ‘honesty’ disappear. For example, even though we know that Google Map Maker is now used in many part

s of the world (see the figure), even in cases when access to vector data is provided by Google, you cannot find out about who contribute, when and where. It is also presented as an authoritative source of information.

Despite the risk of misinterpretation, the assertion can be useful as a reminder that the differences between authoritative and crowdsourced information are not as big as it may seem.

Generally, the average person does not demand high quality data – they even accept lots of garbage on mobile devices as part and parcel of how the world turns.

The real question in my mind and the public is: how much do we trust scientists to tell us what is authoritative today? They see errors climate change debates, conflicting science about medicine and the list goes on.

Mappers need to speak more about uncertainty level as compared to absolute accuracy (though you and I might like that more). Then maybe people would start to understand what fit-for.purpose really means,

One problem is that the word “authoritative” semantically means that data from that source is correct. Once a source is called ¨authoritative¨ (self-proclaimed, de-facto, or as seen by its users), people naturally stop questioning it.

In today´s world, we should just stop using that word, because we usually have many worthy sources for any kind of data. Call them “government-funded”, “highly-regarded” or even “best available for this particular case”. Praise their strong points and warn about their weak points. Compare. Be honest. But dont flag them with a black-and-white term.